Intelligent burn planning

ABSTRACT

An information handling system may include a processor and a memory communicatively coupled to the processor. The information handling system may be configured to: receive, for each of a plurality of target information handling systems, information regarding software to be burned to the respective target information handling system; receive, for each of the target information handling systems, information regarding testing time; based on a statistical analysis of the information regarding the testing time, determine a predicted burn time for each target information handling system; and based on the respective predicted burn times, determine a desired order in which the target information handling systems are to be burned with the software.

TECHNICAL FIELD

The present disclosure relates in general to information handlingsystems, and more particularly to efficiently installing and configuringsoftware in an information handling system.

BACKGROUND

As the value and use of information continues to increase, individualsand businesses seek additional ways to process and store information.One option available to users is information handling systems. Aninformation handling system generally processes, compiles, stores,and/or communicates information or data for business, personal, or otherpurposes thereby allowing users to take advantage of the value of theinformation. Because technology and information handling needs andrequirements vary between different users or applications, informationhandling systems may also vary regarding what information is handled,how the information is handled, how much information is processed,stored, or communicated, and how quickly and efficiently the informationmay be processed, stored, or communicated. The variations in informationhandling systems allow for information handling systems to be general orconfigured for a specific user or specific use such as financialtransaction processing, airline reservations, enterprise data storage,or global communications. In addition, information handling systems mayinclude a variety of hardware and software components that may beconfigured to process, store, and communicate information and mayinclude one or more computer systems, data storage systems, andnetworking systems.

After an information handling system has been physically built (e.g., bya manufacturer), it may need to have software installed and configuredbefore it can be delivered. This process of installing software on anewly built information handling system is referred to herein as“burning.” In various embodiments, burning may include copying asoftware image to the system, extracting files, executing installationscripts and/or executables, performing software customizations, and/orany subset thereof. In some embodiments, an information handling systemor group of such systems may be inserted into a “burn rack” to carry outthe burning. Efficient utilization of the limited number of locationswithin a burn rack contributes greatly to overall manufacturingthroughput.

A problem has arisen in that existing methods for performing softwareburn can require lengthy and unpredictable amounts of time. Further,timing requirements (e.g., desired shipment dates) for particularsystems or groups of systems may further complicate matters, as may thetime required for testing.

Any failures in testing or burning may increase the dwell time forinformation handling systems in burn racks, reducing overall throughput.

Existing burn planning systems are typically somewhat ad hoc, relying onsubject matter experts with knowledge of expected burn time to performmanual calculations regarding burn rack utilization. Embodiments of thisdisclosure may utilize predicted burn time and component test time inorder to make the process more efficient and more easily automated.

It should be noted that the discussion of a technique in the Backgroundsection of this disclosure does not constitute an admission of prior-artstatus. No such admissions are made herein, unless clearly andunambiguously identified as such.

SUMMARY

In accordance with the teachings of the present disclosure, thedisadvantages and problems associated with installing and configuringsoftware in an information handling system may be reduced or eliminated.

In accordance with embodiments of the present disclosure, an informationhandling system may include a processor and a memory communicativelycoupled to the processor. The information handling system may beconfigured to: receive, for each of a plurality of target informationhandling systems, information regarding software to be burned to therespective target information handling system; receive, for each of thetarget information handling systems, information regarding testing time;based on a statistical analysis of the information regarding the testingtime, determine a predicted burn time for each target informationhandling system; and based on the respective predicted burn times,determine a desired order in which the target information handlingsystems are to be burned with the software.

In accordance with these and other embodiments of the presentdisclosure, a method may include: an information handling systemreceiving, for each of a plurality of target information handlingsystems, information regarding software to be burned to the respectivetarget information handling system; the information handling systemreceiving, for each of the target information handling systems,information regarding testing time; based on a statistical analysis ofthe information regarding the testing time, the information handlingsystem determining a predicted burn time for each target informationhandling system; based on the respective predicted burn times, theinformation handling system determining a desired order in which thetarget information handling systems are to be burned with the software;and the information handling system causing the target informationhandling systems to be burned in the desired order.

In accordance with these and other embodiments of the presentdisclosure, an article of manufacture may include a non-transitory,computer-readable medium having computer-executable code thereon that isexecutable by a processor of an information handling system for:receiving, for each of a plurality of target information handlingsystems, information regarding software to be burned to the respectivetarget information handling system; receiving, for each of the targetinformation handling systems, information regarding testing time; basedon a statistical analysis of the information regarding the testing time,determining a predicted burn time for each target information handlingsystem; and based on the respective predicted burn times, determining adesired order in which the target information handling systems are to beburned with the software.

Technical advantages of the present disclosure may be readily apparentto one skilled in the art from the figures, description and claimsincluded herein. The objects and advantages of the embodiments will berealized and achieved at least by the elements, features, andcombinations particularly pointed out in the claims.

It is to be understood that both the foregoing general description andthe following detailed description are examples and explanatory and arenot restrictive of the claims set forth in this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present embodiments and advantagesthereof may be acquired by referring to the following description takenin conjunction with the accompanying drawings, in which like referencenumbers indicate like features, and wherein:

FIG. 1 illustrates a block diagram of an example information handlingsystem, in accordance with embodiments of the present disclosure;

FIG. 2 illustrates a block diagram of an example architecture, inaccordance with embodiments of the present disclosure;

FIG. 3 illustrates an example heat map showing statistical correlations,in accordance with embodiments of the present disclosure;

FIG. 4 illustrates an example scatter plot and linear regression, inaccordance with embodiments of the present disclosure;

FIG. 5 illustrates an example data table, in accordance with embodimentsof the present disclosure;

FIG. 6 illustrates a block diagram of an example set of burn racklocations, in accordance with embodiments of the present disclosure; and

FIG. 7 illustrates a block diagram of an example set of burn racklocations, in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION

Preferred embodiments and their advantages are best understood byreference to FIGS. 1 through 7, wherein like numbers are used toindicate like and corresponding parts.

For the purposes of this disclosure, the term “information handlingsystem” may include any instrumentality or aggregate ofinstrumentalities operable to compute, classify, process, transmit,receive, retrieve, originate, switch, store, display, manifest, detect,record, reproduce, handle, or utilize any form of information,intelligence, or data for business, scientific, control, entertainment,or other purposes. For example, an information handling system may be apersonal computer, a personal digital assistant (PDA), a consumerelectronic device, a network storage device, or any other suitabledevice and may vary in size, shape, performance, functionality, andprice. The information handling system may include memory, one or moreprocessing resources such as a central processing unit (“CPU”) orhardware or software control logic. Additional components of theinformation handling system may include one or more storage devices, oneor more communications ports for communicating with external devices aswell as various input/output (“I/O”) devices, such as a keyboard, amouse, and a video display. The information handling system may alsoinclude one or more buses operable to transmit communication between thevarious hardware components.

For purposes of this disclosure, when two or more elements are referredto as “coupled” to one another, such term indicates that such two ormore elements are in electronic communication or mechanicalcommunication, as applicable, whether connected directly or indirectly,with or without intervening elements.

When two or more elements are referred to as “coupleable” to oneanother, such term indicates that they are capable of being coupledtogether.

For the purposes of this disclosure, the term “computer-readable medium”(e.g., transitory or non-transitory computer-readable medium) mayinclude any instrumentality or aggregation of instrumentalities that mayretain data and/or instructions for a period of time. Computer-readablemedia may include, without limitation, storage media such as a directaccess storage device (e.g., a hard disk drive or floppy disk), asequential access storage device (e.g., a tape disk drive), compactdisk, CD-ROM, DVD, random access memory (RAM), read-only memory (ROM),electrically erasable programmable read-only memory (EEPROM), and/orflash memory; communications media such as wires, optical fibers,microwaves, radio waves, and other electromagnetic and/or opticalcarriers; and/or any combination of the foregoing.

For the purposes of this disclosure, the term “information handlingresource” may broadly refer to any component system, device, orapparatus of an information handling system, including withoutlimitation processors, service processors, basic input/output systems,buses, memories, I/O devices and/or interfaces, storage resources,network interfaces, motherboards, and/or any other components and/orelements of an information handling system.

For the purposes of this disclosure, the term “management controller”may broadly refer to an information handling system that providesmanagement functionality (typically out-of-band managementfunctionality) to one or more other information handling systems. Insome embodiments, a management controller may be (or may be an integralpart of) a service processor, a baseboard management controller (BMC), achassis management controller (CMC), or a remote access controller(e.g., a Dell Remote Access Controller (DRAC) or Integrated Dell RemoteAccess Controller (iDRAC)).

FIG. 1 illustrates a block diagram of an example information handlingsystem 102, in accordance with embodiments of the present disclosure. Insome embodiments, information handling system 102 may comprise a serverchassis configured to house a plurality of servers or “blades.” In otherembodiments, information handling system 102 may comprise a personalcomputer (e.g., a desktop computer, laptop computer, mobile computer,and/or notebook computer). In yet other embodiments, informationhandling system 102 may comprise a storage enclosure configured to housea plurality of physical disk drives and/or other computer-readable mediafor storing data (which may generally be referred to as “physicalstorage resources”). As shown in FIG. 1, information handling system 102may comprise a processor 103, a memory 104 communicatively coupled toprocessor 103, a BIOS 105 (e.g., a UEFI BIOS) communicatively coupled toprocessor 103, a network interface 108 communicatively coupled toprocessor 103, and a management controller 112 communicatively coupledto processor 103.

In operation, processor 103, memory 104, BIOS 105, and network interface108 may comprise at least a portion of a host system 98 of informationhandling system 102. In addition to the elements explicitly shown anddescribed, information handling system 102 may include one or more otherinformation handling resources.

Processor 103 may include any system, device, or apparatus configured tointerpret and/or execute program instructions and/or process data, andmay include, without limitation, a microprocessor, microcontroller,digital signal processor (DSP), application specific integrated circuit(ASIC), or any other digital or analog circuitry configured to interpretand/or execute program instructions and/or process data. In someembodiments, processor 103 may interpret and/or execute programinstructions and/or process data stored in memory 104 and/or anothercomponent of information handling system 102.

Memory 104 may be communicatively coupled to processor 103 and mayinclude any system, device, or apparatus configured to retain programinstructions and/or data for a period of time (e.g., computer-readablemedia). Memory 104 may include RAM, EEPROM, a PCMCIA card, flash memory,magnetic storage, opto-magnetic storage, or any suitable selectionand/or array of volatile or non-volatile memory that retains data afterpower to information handling system 102 is turned off.

As shown in FIG. 1, memory 104 may have stored thereon an operatingsystem (OS) 106. Operating system 106 may comprise any program ofexecutable instructions (or aggregation of programs of executableinstructions) configured to manage and/or control the allocation andusage of hardware resources such as memory, processor time, disk space,and input and output devices, and provide an interface between suchhardware resources and application programs hosted by operating system106. In addition, operating system 106 may include all or a portion of anetwork stack for network communication via a network interface (e.g.,network interface 108 for communication over a data network). Althoughoperating system 106 is shown in FIG. 1 as stored in memory 104, in someembodiments operating system 106 may be stored in storage mediaaccessible to processor 103, and active portions of operating system 106may be transferred from such storage media to memory 104 for executionby processor 103.

Network interface 108 may comprise one or more suitable systems,apparatuses, or devices operable to serve as an interface betweeninformation handling system 102 and one or more other informationhandling systems via an in-band network. Network interface 108 mayenable information handling system 102 to communicate using any suitabletransmission protocol and/or standard. In these and other embodiments,network interface 108 may comprise a network interface card, or “NIC.”In these and other embodiments, network interface 108 may be enabled asa local area network (LAN)-on-motherboard (LOM) card.

Management controller 112 may be configured to provide managementfunctionality for the management of information handling system 102.Such management may be made by management controller 112 even ifinformation handling system 102 and/or host system 98 are powered off orpowered to a standby state. Management controller 112 may include aprocessor 113, memory, and a network interface 118 separate from andphysically isolated from network interface 108.

As shown in FIG. 1, processor 113 of management controller 112 may becommunicatively coupled to processor 103. Such coupling may be via aUniversal Serial Bus (USB), System Management Bus (SMBus), and/or one ormore other communications channels.

Network interface 118 may be coupled to a management network, which maybe separate from and physically isolated from the data network as shown.Network interface 118 of management controller 112 may comprise anysuitable system, apparatus, or device operable to serve as an interfacebetween management controller 112 and one or more other informationhandling systems via an out-of-band management network. Networkinterface 118 may enable management controller 112 to communicate usingany suitable transmission protocol and/or standard. In these and otherembodiments, network interface 118 may comprise a network interfacecard, or “NIC.” Network interface 118 may be the same type of device asnetwork interface 108, or in other embodiments it may be a device of adifferent type.

As noted above, newly constructed information handling systems maytypically need to have software burned before they can be delivered to abuyer. Embodiments of this disclosure may provide for determining (e.g.,via an information handling system such as information handling system102 ) a more efficient process for doing so. In these and otherembodiments, the process of burning software to a plurality of “target”information handling systems may be automated via an informationhandling system such as information handling system 102.

For example, the order in which target information handling systemsreceive their software may be optimized to reduce the overall burningtime. In some embodiments, this may be accomplished by burning thetarget information handling systems in an order that is determined suchthat systems having the same (or similar) predicted burn finish timeswill be burned concurrently.

Turning now to FIG. 2, a block diagram of a system architecture isshown, in accordance with some embodiments.

As shown in FIG. 2, decision maker module 206 may be the core engineconfigured to define the priority of incoming burn requests, as well asto help to allocate space efficiently on burn rack(s). Real time channelmodule 202 may be configured to interface with and accept messages fromvarious sources (e.g., factory planning and operation messages). Resultstore module 204 may include a storage mechanism to hold predictedvalues and real-time processed information, providing feedback to theoverall burn planning engine. Finally, external systems 208 may alsoprovide input to result store module 204 such as expected burn time,test time, and manufacturing time for each order. As shown, any or allof these various data sources may be combined within the priorityalgorithms of decision maker module 206 in order to define the priorityof each unit classification (which may refer to a logical name for agroup of models or service tags having the same or similar test and burntime). In addition to defining the priority ordering, decision makermodule 206 may also determine an efficient way of populating theavailable burn rack locations.

Real time channel module 202 may include, for example, informationregarding a factory planned order (e.g., a list of orders from a factoryplanning system indicating what needs to be built within a specifiedtime). Real time channel module 202 may further include informationregarding a priority order (e.g., a business-defined ordering ofpriorities). Real time channel module 202 may further includeinformation regarding burn events and results (e.g., events generated bythe burn system to provide insight on software burn progress on the burnrack). Real time channel module 202 may further include informationregarding system unit test events and results. Finally, real timechannel module 202 may include information regarding burn rackutilization.

External systems 208 may provide data regarding predicted burn times(based on a burn prediction model), predicted test times (based on asystem unit test model), and predicted unit build time for how long theassembly of each system is likely to take.

In general, any type of testing may be used to validate hardware,software, firmware, etc. of an information handling system. In someembodiments, hardware tests may be used and may be conducted prior toburning. For the sake of concreteness within this disclosure, three setsof testing are referred to: a quick test (QT), an extended test 1 (ET1), and an extended test 2 (ET2 ). Typically, different types of testingmay be grouped into ET1 versus ET2. For example, in some embodiments,ET1 may focus mostly on storage media, and ET2 may focus mostly onnetwork issues. As discussed in more detail below, correlations mayexist between the amounts of time required for different types of testand the amount of time required for software burning. These correlationsmay be exploited in some embodiments to arrive at more accurateestimates for predicted burn times. In these and other embodiments, anAI-based system may define a priority for the units that need to beburned first by taking into account the predicted burn time and thepredicted test time of any given type of unit.

Raw data (e.g., factory planned order, business defined priority orders,manufacturing operational data such as burn and test status) may be fedinto result store module 204. The result store module 204 may use unitclassifiers to classify input data by defining similarities (e.g., timeto burn or time to test) based on characteristics of informationhandling systems such as the line of business, system family, OS type,OS part number, and physical configuration information such as harddrive size and speed, RAM, CPU speed, etc.

This processed data from result store module 204 may then be passed todecision maker module 206 to decide which unit to burn first. Decisionmaker module 206 may execute various algorithm to prioritize each unitclassification. Decision maker module 206 also may rely on externalsystem 208 to provide input such as expected burn time, test time, andprojected unit build time.

Upon analyzing different sets of observations, it may be identified thatdifferent types of systems take different amounts of time to test and/orburn. System requiring similar times may then be grouped together sothat they can be burned simultaneously in a batch. This grouping ofservice tags into a group having similar expected test and/or burn timesis referred to herein as unit classification.

In some cases, a statistical analysis of the time required for testingand/or burning previous embodiments of a given configuration ofinformation handling system may be performed. For example, a largestandard deviation in the time required for some particularconfiguration may indicate unpredictability, and this may then be takeninto account when planning for testing and/or burning machines of thatconfiguration.

Additionally, various correlations between test times and/or burn timesmay give further information. For example, as one of ordinary skill inthe art with the benefit of this disclosure will appreciate, calculationof Pearson correlation coefficients (e.g., pairwise for each pair ofdata sets) may allow exploration of such correlations and provideimproved estimates for predicted burn time. An equation for calculatingthe Pearson correlation coefficient for two quantities x and y is givenby:

$r_{xy} = \frac{\sum_{i = 1}^{n}{\left( {x_{i} - \overset{¯}{x}} \right)\left( {y_{i} - \overset{¯}{y}} \right)}}{\left( {n - 1} \right)s_{x}s_{y}}$

where x and y are the means of x and y, and where s_(x) and s_(y) arethe standard deviations.

Turning now to FIG. 3, a heat map 300 of Pearson correlationcoefficients is shown for an example data set. As shown, for thisexample data set, the burn duration correlates positively with theduration of extended test 1, negatively with the duration of extendedtest 2, and only slightly with the duration of the quick test. (Thecorrelations along the diagonal are of course equal to 1.) Heat mapssuch as example heat map 300 may provide a novel way of studying burnduration patterns across all unit classifications, allowing moreaccurate predictions of burn time and more efficient utilization of burnracks.

For example, with the knowledge that extended test 1 duration correlatespositively with burn duration, a linear regression allows forcalculation of an estimated burn time for a system, given the time thatit required for extended test 1. FIG. 4 provides a plot of an examplelinear regression 400. Similar regressions may be run for other testsuites as well, such as for extended test 2 and quick test. Thus,multiple estimates for a predicted burn time may be calculated based onthese multiple linear regressions (or other types of regressions). Invarious embodiments, these multiple estimates may be averaged, or thehistorically most reliable estimate may be selected. In otherembodiments, a more sophisticated model may be constructed to determinethe predicted burn time by combining the various estimates in otherways.

In some cases, a large amount of noise may be present inhigh-dimensional data sets. To discard unhelpful features and help buildmore generalized models, feature selection techniques may be used toseek a reduced subset of features that improve the performance of thelearning algorithm. Heat map based feature selection algorithms may beused to estimate the importance of a feature based on its interactionwith different variables such as ET1, ET2, QT, and burn time.

In some embodiments, a problem may arise in which data becomes sparse ina high-dimensional space, which may cause issues with algorithms thatare not designed to handle such complex spaces. To achieve the goal ofdimensionality reduction, techniques such as principal componentanalysis (PCA) may be employed. The feature extraction approach seeks totransform the high-dimensional features into a whole new space of lowerdimensionality by a linear combination of the original set of features.

With all of this in mind, a burn scheduling algorithm may be devised.This may be constructed as a batch processing algorithm where each burnrequest is assigned a priority. Arrival sequence may be decided based onstandard deviation, predicted burn time, predicted test time, orderquantity, manufacturing build time, and existing unit classificationswith higher priorities.

While determining an arrival time at a burn rack, the system mayconsider estimated potential new locations in the rack as they becomeavailable, and uncertainty figures may be propagated forward in suchestimates. This uncertainty may be based in part on the standarddeviation of test times, as discussed above. Once a scatter plot isdefined, the system may filter out any outlier units that took farlonger or far shorter amounts of time than the rest of the data set. Allof these outliers may be examined to identify influencing factors, suchas failures (e.g., test failures or build failures) or patterns inprevious or current processes.

Turning now to FIG. 5, table 500 is shown with an example set of burnpriority data. In table 500, request ID is a unique identifier given toeach request for a system to be burned. Classification name is shown inthis example as a simple service tag identifier, but in otherembodiments it may be an AI-based name given to systems having the sameconfiguration (e.g., an identifier incorporating information about theparticular line of business, family, OS type, OS part number, andphysical configuration). Quantity refers to the order quantity thatshould be shipped together. Request time refers to the time when thefactory planning application released an order to be built. Facilityship time refers to the time when the system is supposed to be shippedout from the factory. Predicted test time refers to the total predictedamount of time for all tests to complete, based on the classificationname. Predicted burn time refers to the predicted amount of time forburning to complete, based on the classification name. Test/burn failcount is a representation of the total number of failures, and may beincremented whenever failure notifications are received. Expected burnend time on rack refers to the predicted finish time for the burn, andmay be calculated both for units which are actually connected to theburn rack, as well as units which are still in the planning stage.

Expected burn time may be calculated as the sum of the predicted testtime and the predicted burn time. Burn stage time may be recalculated ifthe system receives any failure notification from test or burn system.

As noted in table 500, the expected test time and expected burn time maybe augmented to take into account the standard deviations of thosequantities. For example, if a particular configuration has a largestandard deviation in its burn time, this may be an indication that alarge amount of “buffer” time should be added to its expected burn timeto account for the uncertainty. In some embodiments, this may beaddressed by adding the standard deviation to the respective quantity.

Further, in the embodiment of table 500, burn priority may be set basedon the predicted total amount of time (build time, burn time, and testtime) that a system is expected to take. The longer that total amount oftime is, the sooner the system should begin burning. Thus the burnpriority column of table 500 is calculated. For example, in thisinstance, request R1 has the highest priority because it is expected totake the largest total amount of time.

In addition to determining an improved priority order for burn requests,some embodiments of this disclosure may also assist in planning whichlocations in a burn rack should be used by which systems.

Turning now to FIG. 6, an annotated table 600 is shown, providing anexample set of locations in a burn rack. As shown, each location in theburn rack may be associated with an entry in a data table: for emptylocations, a rack lookup table may provide information on a system readyto burn; and for occupied locations, a rack occupancy table may provideinformation on the system that is being burned.

Various algorithms for populating racks may be used, in variousembodiments.

In some embodiments, a partitioned allocation algorithm may be used.This may include creating logical groups based on expected burncompletion time. When one or more rack locations are free within a slotto accommodate a new burn request, this grouping may be used by priorityscheduling algorithm to plan against demand. FIG. 7 provides anannotated table 700, in which free locations are shown in white, andoccupied locations are shown darkened.

In these and other embodiments, a best rack match algorithm may be used.This may include a process to allocate a burn request (e.g. R1 fromtable 500 ) into a selected rack space by determining the number ofunits that fit into either an available rack space or an occupied rackspace. (Allocating units into an occupied rack space may add theexpected burn completion time of the current burning-in-progress unit tothe total burn time.) If the unit's total time is within the desiredfacility ship time, priority may be is assigned to the burn request.

In these and other embodiments, a placement algorithm may be used. Thismay include a rack allocation algorithm that searches for high-priorityrequests and matches them against available free slots based onclassification need. In this algorithm, the rack allocator may keeptrack of free slots, and upon receiving a request for burn planning, mayscan through the list for the best slot that is large enough to satisfythe request. If the chosen slot is significantly larger than thatrequested, the remainder may be added to another free slot.

Various embodiments of this disclosure may provide many benefits. Forexample, system with the same or similar burn time may be plannedtogether, reducing dwell time in burn racks. Further, defining an orderclassification based on test and burn time may influence order capacityplanning. Further, the use of a real-time progress feed from testing andburning systems may reduce dwell time in case of failure.

In these and other embodiments, software burning for large orderquantities may be planned in a single batch or multiple batches,depending on availability. Service tags from the same order may be putnear to one another in a rack for burning, which may help an operator toeasily pick all service tag in a given order for shipment.

The described reductions in burn time may in turn reduce the overallend-to-end manufacturing cycle time.

Embodiments of this disclosure may provide end users flexibility foraddition of priority orders to initiate burn process. Further, real-timeinformation may be collected automatically and continually, which mayhelp in discrete-event simulation and burn performance.

Although various possible advantages with respect to embodiments of thisdisclosure have been described, one of ordinary skill in the art withthe benefit of this disclosure will understand that in any particularembodiment, not all of such advantages may be applicable. In anyparticular embodiment, some, all, or even none of the listed advantagesmay apply.

This disclosure encompasses all changes, substitutions, variations,alterations, and modifications to the exemplary embodiments herein thata person having ordinary skill in the art would comprehend. Similarly,where appropriate, the appended claims encompass all changes,substitutions, variations, alterations, and modifications to theexemplary embodiments herein that a person having ordinary skill in theart would comprehend. Moreover, reference in the appended claims to anapparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, or component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative.

Further, reciting in the appended claims that a structure is “configuredto” or “operable to” perform one or more tasks is expressly intended notto invoke 35 U.S.C. § 112 (f) for that claim element. Accordingly, noneof the claims in this application as filed are intended to beinterpreted as having means-plus-function elements. Should Applicantwish to invoke § 112 (f) during prosecution, Applicant will recite claimelements using the “means for [performing a function]” construct.

All examples and conditional language recited herein are intended forpedagogical objects to aid the reader in understanding the invention andthe concepts contributed by the inventor to furthering the art, and areconstrued as being without limitation to such specifically recitedexamples and conditions. Although embodiments of the present inventionshave been described in detail, it should be understood that variouschanges, substitutions, and alterations could be made hereto withoutdeparting from the spirit and scope of the disclosure.

1. An information handling system comprising: a processor; and a memorycommunicatively coupled to the processor; wherein the informationhandling system is configured to: receive, for each of a plurality oftarget information handling systems, information regarding software tobe burned to the respective target information handling system; receive,for each of the target information handling systems, informationregarding testing time; based on a statistical analysis of theinformation regarding the testing time, determine a predicted burn timefor each target information handling system; and based on the respectivepredicted burn times, determine a desired order in which the targetinformation handling systems are to be burned with the software, whereinthe desired order is determined such that systems having similarpredicted burn times are burned concurrently.
 2. The informationhandling system of claim 1, wherein the desired order is further basedon an indication that a particular one of the target informationhandling systems is associated with a high business priority.
 3. Theinformation handling system of claim 1, wherein the statistical analysisincludes an indication that the predicted burn time is correlated with aselected testing time.
 4. The information handling system of claim 3,wherein the information handling system is further configured todetermine a standard deviation for at least one of the predicted burntime or the selected testing time.
 5. The information handling system ofclaim 4, wherein the information handling system is further configuredto determine the desired order based on a sum of the standard deviationwith the respective at least one of the predicted burn time or theselected testing time.
 6. The information handling system of claim 3,wherein the correlation is a positive correlation.
 7. The informationhandling system of claim 3, wherein the correlation is a negativecorrelation.
 8. The information handling system of claim 1, wherein theinformation handling system is further configured to determine desiredlocations in a burn rack for the target information handling systems. 9.A method comprising: an information handling system receiving, for eachof a plurality of target information handling systems, informationregarding software to be burned to the respective target informationhandling system; the information handling system receiving, for each ofthe target information handling systems, information regarding testingtime; based on a statistical analysis of the information regarding thetesting time, the information handling system determining a predictedburn time for each target information handling system; based on therespective predicted burn times, the information handling systemdetermining a desired order in which the target information handlingsystems are to be burned with the software, wherein the desired order isdetermined such that systems having similar predicted burn times areburned concurrently; and the information handling system causing thetarget information handling systems to be burned in the desired order.10. The method of claim 9, wherein the statistical analysis includes anindication that the predicted burn time is correlated with a selectedtesting time.
 11. The method of claim 10, wherein the desired order isbased on a total predicted amount of time for testing and burning eachrespective target information handling system.
 12. The method of claim11, wherein longer total predicted amounts of time are associated withearlier burn times in the desired order.
 13. An article of manufacturecomprising a non-transitory, computer-readable medium havingcomputer-executable code thereon that is executable by a processor of aninformation handling system for: receiving, for each of a plurality oftarget information handling systems, information regarding software tobe burned to the respective target information handling system;receiving, for each of the target information handling systems,information regarding testing time; based on a statistical analysis ofthe information regarding the testing time, determining a predicted burntime for each target information handling system; and based on therespective predicted burn times, determining a desired order in whichthe target information handling systems are to be burned with thesoftware, wherein the desired order is determined such that systemshaving similar predicted burn times are burned concurrently.
 14. Thearticle of claim 13, wherein the desired order is further based on anindication that a particular one of the target information handlingsystems is associated with a high business priority.
 15. The article ofclaim 13, wherein the statistical analysis includes an indication thatthe predicted burn time is correlated with a selected testing time. 16.The article of claim 15, wherein the code is further executable fordetermining a standard deviation for at least one of the predicted burntime or the selected testing time.
 17. The article of claim 16, whereinthe code is further executable for determining the desired order basedon a sum of the standard deviation with the respective at least one ofthe predicted burn time or the selected testing time.
 18. The article ofclaim 15, wherein the correlation is a positive correlation.
 19. Thearticle of claim 15, wherein the correlation is a negative correlation.20. The article of claim 13, wherein the code is further executable for:determining desired locations in a burn rack for the target informationhandling systems; and causing the target information handling systems tobe burned in the desired order in the desired locations.